A hybrid Machine learning approach for monitoring wheat crop traits using Proximal Hyperspectral remote sensing

The ability of proximal hyperspectral sensors to capture precise spectral measurements, which signify the inherent properties of the target material, presents a strong potential for accurately estimating key crop health indicators in precision agriculture. This study employs a hybrid methodology that integrates a physical process-based radiative transfer (RT) model and machine learning regression to assess three key wheat crop traits: leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The non-imaging hyperspectral data collected proximally using the ASD FieldSpec Spectroradiometer were spectrally resampled to 269 spectral bands ranging from 400 to 1000 nm for the retrieval of these crop traits. A hybrid retrieval workflow was developed using a Gaussian process regression algorithm, an active learning method for reducing sample size, principal component analysis for spectral dimensionality reduction, and training with spectral simulations from the PROSAIL RT model. Upon validating against in-situ measurements, good accuracies in terms of NRMSE values, 10.65%, 11.63%, and 13.85%, were achieved for LAI, LCC, and CCC, respectively. Plot-wise maps showing the spatial variability of LAI, LCC, and CCC, along with their uncertainties, were also generated to visualize the prediction results. These optimised retrieval models facilitate operational delivery of critical variables for monitoring crop dynamics by facilitating efficient nutrient management practices.